{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from os.path import basename\n",
    "\n",
    "import cPickle\n",
    "import h5py\n",
    "import yaml\n",
    "\n",
    "# plt.rc('text', usetex=True)\n",
    "# plt.rc('font', family='serif')\n",
    "# matplotlib.rcParams['text.usetex']=False\n",
    "# matplotlib.rcParams['text.latex.unicode']=True\n",
    "\n",
    "iterations_per_epoch = 17296\n",
    "\n",
    "from matplotlib import cm\n",
    "\n",
    "def get_colors(number_of_lines):\n",
    "    start = 0.0\n",
    "    stop = 1.0\n",
    "    cm_subsection = np.linspace(start, stop, number_of_lines) \n",
    "\n",
    "    colors = [ cm.viridis(x) for x in cm_subsection ]\n",
    "    colors = colors[::-1]\n",
    "    return colors\n",
    "\n",
    "\n",
    "\n",
    "def plot_curves(datadir, exps, plot_train_loss=True, figsize=(10,10), colors=None, linewidth=2, xunits='epochs', downsample=1):\n",
    "    fig=plt.figure(figsize=figsize)\n",
    "    for iexp, exp in enumerate(exps):\n",
    "\n",
    "        def get_x(nepochs, xunits, downsample):\n",
    "            if xunits == 'epochs':\n",
    "                return (np.arange(nepochs)+1)/float(downsample)\n",
    "            elif xunits == 'iterations':\n",
    "                x = np.arange(nepochs)+1\n",
    "                return iterations_per_epoch*x/float(downsample)\n",
    "        \n",
    "        # plot the learning curves\n",
    "        histfile=datadir + '/history/' + exp['histfile']\n",
    "        label=exp['label']\n",
    "        with open(histfile,'rb') as f:\n",
    "            hist = cPickle.load(f)\n",
    "        val_loss = hist['on_epoch_end']['val_loss']\n",
    "        if colors is None:\n",
    "            x=get_x(len(val_loss), xunits, downsample)\n",
    "            p=plt.plot(x, val_loss, label=label, linewidth=linewidth)\n",
    "        else:\n",
    "            x=get_x(len(val_loss), xunits, downsample)\n",
    "            p=plt.plot(x, val_loss, label=label, color=colors[iexp], linewidth=linewidth)\n",
    "        if plot_train_loss:\n",
    "            train_loss=hist['on_epoch_end']['loss']\n",
    "            x=get_x(len(train_loss), xunits, downsample)\n",
    "            plt.plot(x, train_loss, color=p[0].get_color(), linestyle=':', linewidth=linewidth+1)\n",
    "\n",
    "        # count the trainable weights of the model\n",
    "        modelfile = datadir + '/models/' + basename(histfile).replace('history','model') + '.hdf5'\n",
    "        f = h5py.File(modelfile)\n",
    "        paramsfile = datadir + '/configs/' + basename(histfile).replace('history', 'params') + '.yaml'\n",
    "        with open(paramsfile,'rb') as fp:\n",
    "            params = yaml.load(fp)\n",
    "        num_params = 0\n",
    "        for key in f.keys():\n",
    "            for key2 in f[key]:\n",
    "                for key3 in f[key][key2].keys():\n",
    "                    #print f[key][key2][key3]\n",
    "                    if 'params_trainable' in params.keys():\n",
    "\n",
    "                        params_trainable = params['params_trainable'] + ['h0']\n",
    "\n",
    "                        param_is_trainable = False\n",
    "                        for name in params_trainable:\n",
    "                            # for each name of the trainable weights\n",
    "                            if name in key3:\n",
    "                                # only count these weights as trainable if the parameter is trainable\n",
    "                                param_is_trainable = True\n",
    "                                #print \"%s is trainable, since it contains %s\" % (key3,name)\n",
    "\n",
    "                        if not param_is_trainable:\n",
    "                            continue\n",
    "\n",
    "                    # print \"Trainable weight %s has shape %s with size %d\" % (key3,f[key][key2][key3].shape,np.prod(f[key][key2][key3].shape))\n",
    "                    num_params = num_params + np.prod(f[key][key2][key3].shape)\n",
    "\n",
    "        print \"Model '%s' has %d trainable weights, best val_loss is %f\" % (label,num_params,min(val_loss))\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 'LSTM, K=2, N=54' has 105071 trainable weights, best val_loss is 0.051167\n",
      "Model 'LSTM, K=2, N=244' has 1030181 trainable weights, best val_loss is 0.048116\n",
      "Model 'DR-SNMF, K=2, N=100' has 103002 trainable weights, best val_loss is 0.036235\n",
      "Model 'DR-SNMF, K=2, N=1000' has 1030002 trainable weights, best val_loss is 0.035428\n",
      "Model 'LSTM, K=5, N=70' has 268007 trainable weights, best val_loss is 0.054158\n",
      "Model 'LSTM, K=5, N=250' has 2576507 trainable weights, best val_loss is 0.056552\n",
      "Model 'DR-SNMF, K=5, N=100' has 257205 trainable weights, best val_loss is 0.033210\n",
      "Model 'DR-SNMF, K=5, N=1000' has 2572005 trainable weights, best val_loss is 0.031583\n",
      "Model 'LSTM, K=2, N=54' has 105071 trainable weights, best val_loss is 0.040835\n",
      "Model 'LSTM, K=2, N=244' has 1030181 trainable weights, best val_loss is 0.033863\n",
      "Model 'DR-SNMF, K=2, N=100' has 103002 trainable weights, best val_loss is 0.032010\n",
      "Model 'DR-SNMF, K=2, N=1000' has 1030002 trainable weights, best val_loss is 0.029525\n",
      "Model 'LSTM, K=5, N=70' has 268007 trainable weights, best val_loss is 0.042590\n",
      "Model 'LSTM, K=5, N=250' has 2576507 trainable weights, best val_loss is 0.034364\n",
      "Model 'DR-SNMF, K=5, N=100' has 257205 trainable weights, best val_loss is 0.028558\n",
      "Model 'DR-SNMF, K=5, N=1000' has 2572005 trainable weights, best val_loss is 0.026553\n"
     ]
    },
    {
     "data": {
      "image/png": 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5QISI7AXup2go8joQJCLbMRTLf1R1S2XtZdkLAJAUB3mp9xAd+DtgbE4Su7H0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\nFYLFYnH5f7Q243C5JTBbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8d00b6c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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R4HtgWE0nzDiShcMVAGYPIS0rC3dhks/CpFNj2tHCorb4M8twO/Ah0Nn8fCAi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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8d088c950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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CAgMHDizRz0RlEoyS8kPgNxH50jq/huNTkzUGp8MDQJbbkl2ai1FpVB8DHddV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\nSyvr2FqmgCTK/cdiSdO+fg0eHwtWb6FJob38rDvBqQXzqi2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k4oPU52dFpKeIWA72qloR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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8d06ca990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Jjo5m6NChZQoef+KLkvJ9YJmIfGH+vpzCpck6g3cEkfMLpJrxGjSz1toDMNQ2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb91f5279d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# training data downsampled by 10\n",
    "\n",
    "plots=[{'datadir':'data_setup_db3355248efc7ce949ff0bc5206f0a81',\n",
    "        'downsample':10,\n",
    "        'xunits':'iterations',\n",
    "        'xlim':[0,1.05e6],\n",
    "        'fig_name':'figures/learning_curves_K2_ds10_3-5x3-5.pdf',\n",
    "        'title':'Learning curves, K=2, 10% of train',\n",
    "        'exps': [ {'histfile':'history_lstm_46666e232751074bd609167dc440df8c',\n",
    "                   'label': 'LSTM, K=2, N=54'},\n",
    "                  {'histfile':'history_lstm_b6da76df68cf530d091aa499d61143de',\n",
    "                   'label': 'LSTM, K=2, N=244'},\n",
    "                  {'histfile':'history_unfolded_snmf_a45e86a1cc146e1e9d7a7f8100d9d2d7',\n",
    "                   'label': 'DR-SNMF, K=2, N=100'},\n",
    "                  {'histfile':'history_unfolded_snmf_a23657edf96a44331501d773db837a1c',\n",
    "                   'label': 'DR-SNMF, K=2, N=1000'},\n",
    "                ]\n",
    "        },\n",
    "        {'datadir':'data_setup_db3355248efc7ce949ff0bc5206f0a81',\n",
    "         'downsample':10,\n",
    "         'xunits':'iterations',\n",
    "         'xlim':[0,1.05e6],\n",
    "         'fig_name':'figures/learning_curves_K5_ds10_3-5x3-5.pdf',\n",
    "         'title':'Learning curves, K=5, 10% of train',\n",
    "         'exps': [ {'histfile':'history_lstm_6a4fc9017283c9f89380f765a60087ce',\n",
    "                   'label': 'LSTM, K=5, N=70'},\n",
    "                  {'histfile':'history_lstm_4561bd13e267026c3f3d1c936b15f709',\n",
    "                   'label': 'LSTM, K=5, N=250'},\n",
    "                  {'histfile':'history_unfolded_snmf_ea1e7d485421e527486476ef696da2da',\n",
    "                   'label': 'DR-SNMF, K=5, N=100'},\n",
    "                  {'histfile':'history_unfolded_snmf_364ccd17a3e187bcccd30cfaa6bd9422',\n",
    "                   'label': 'DR-SNMF, K=5, N=1000'}\n",
    "                 ]\n",
    "        },\n",
    "        {'datadir':'data_setup_cc061d1dc474f44165340bb36f11b16d',\n",
    "        'downsample':1,\n",
    "        'xunits':'iterations',\n",
    "        'xlim':[0,3.2e6],\n",
    "        'fig_name':'figures/learning_curves_K2_ds1_3-5x3-5.pdf',\n",
    "        'title':'Learning curves, K=2, 100% of train',\n",
    "        'exps': [ {'histfile':'history_lstm_46666e232751074bd609167dc440df8c',\n",
    "                   'label': 'LSTM, K=2, N=54'},\n",
    "                  {'histfile':'history_lstm_b6da76df68cf530d091aa499d61143de',\n",
    "                   'label': 'LSTM, K=2, N=244'},\n",
    "                  {'histfile':'history_unfolded_snmf_a45e86a1cc146e1e9d7a7f8100d9d2d7',\n",
    "                   'label': 'DR-SNMF, K=2, N=100'},\n",
    "                  {'histfile':'history_unfolded_snmf_a23657edf96a44331501d773db837a1c',\n",
    "                   'label': 'DR-SNMF, K=2, N=1000'},\n",
    "                ]\n",
    "        },\n",
    "        {'datadir':'data_setup_cc061d1dc474f44165340bb36f11b16d',\n",
    "         'downsample':1,\n",
    "         'xunits':'iterations',\n",
    "         'xlim':[0,3.2e6],\n",
    "         'fig_name':'figures/learning_curves_K5_ds1_3-5x3-5.pdf',\n",
    "         'title':'Learning curves, K=5, 100% of train',\n",
    "         'exps': [ {'histfile':'history_lstm_6a4fc9017283c9f89380f765a60087ce',\n",
    "                   'label': 'LSTM, K=5, N=70'},\n",
    "                  {'histfile':'history_lstm_4561bd13e267026c3f3d1c936b15f709',\n",
    "                   'label': 'LSTM, K=5, N=250'},\n",
    "                  {'histfile':'history_unfolded_snmf_ea1e7d485421e527486476ef696da2da',\n",
    "                   'label': 'DR-SNMF, K=5, N=100'},\n",
    "                  {'histfile':'history_unfolded_snmf_364ccd17a3e187bcccd30cfaa6bd9422',\n",
    "                   'label': 'DR-SNMF, K=5, N=1000'}\n",
    "                 ]\n",
    "        },\n",
    "]\n",
    "\n",
    "for iplot, plot in enumerate(plots):\n",
    "\n",
    "    datadir=plot['datadir']\n",
    "    downsample=plot['downsample']\n",
    "    xunits=plot['xunits']\n",
    "    xlim=plot['xlim']\n",
    "\n",
    "    fig_name=plot['fig_name']\n",
    "    title=plot['title']\n",
    "    \n",
    "    exps=plot['exps']\n",
    "\n",
    "    colors=get_colors(4)\n",
    "\n",
    "    only_big_models=False\n",
    "    if only_big_models:\n",
    "        exps=[exps[1],exps[3]]\n",
    "        colors=get_colors(2)\n",
    "\n",
    "    plot_train_loss = True\n",
    "    ylim=None\n",
    "    ylim=[0.0,0.11]\n",
    "\n",
    "    fig=plot_curves(datadir, exps, plot_train_loss=plot_train_loss, figsize=(3.5,3.5), colors=colors, xunits='iterations', downsample=downsample)\n",
    "\n",
    "    if xlim is not None:\n",
    "        plt.xlim(xlim)\n",
    "    if ylim is not None:\n",
    "        plt.ylim(ylim)\n",
    "    plt.title(title)\n",
    "    plt.legend()#loc='center left')#, bbox_to_anchor=(1, 0.5))\n",
    "    plt.grid()\n",
    "    if xunits=='epochs':\n",
    "        plt.xlabel('Epochs')\n",
    "    elif xunits=='iterations':\n",
    "        plt.xlabel('Training iterations')\n",
    "        plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n",
    "    plt.ylabel('Train loss or dev. loss')\n",
    "\n",
    "    fig.savefig(fig_name, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 'LSTM, K=5, N=70' has 268007 trainable weights, best val_loss is 0.054158\n",
      "Model 'LSTM, K=5, N=250' has 2576507 trainable weights, best val_loss is 0.056552\n",
      "Model 'DR-SNMF, K=5, N=100' has 257205 trainable weights, best val_loss is 0.033210\n",
      "Model 'DR-SNMF, K=5, N=1000' has 2572005 trainable weights, best val_loss is 0.031583\n"
     ]
    },
    {
     "data": {
      "image/png": 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odoSPgX83rViNT7n5s7c2KkrLdnv4DYVgYOCRQnDGCU9EcydeSVkSwlbDrB6j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\njX82Hy0ZGE4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb8cf86f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# training data downsampled by 10\n",
    "datadir='data_setup_db3355248efc7ce949ff0bc5206f0a81'\n",
    "\n",
    "fig_name='figures/learning_curves_K5_ds10_3-5x3-5.pdf'\n",
    "\n",
    "exps=[ \\\n",
    "#       {'histfile':'history_lstm_46666e232751074bd609167dc440df8c',\n",
    "#        'label': 'LSTM, K=2, N=54'},\n",
    "#       {'histfile':'history_lstm_b6da76df68cf530d091aa499d61143de',\n",
    "#        'label': 'LSTM, K=2, N=244'},\n",
    "      {'histfile':'history_lstm_6a4fc9017283c9f89380f765a60087ce',\n",
    "       'label': 'LSTM, K=5, N=70'},\n",
    "      {'histfile':'history_lstm_4561bd13e267026c3f3d1c936b15f709',\n",
    "       'label': 'LSTM, K=5, N=250'},\n",
    "#       {'histfile':'history_unfolded_snmf_a45e86a1cc146e1e9d7a7f8100d9d2d7',\n",
    "#        'label': 'DR-SNMF, K=2, N=100'},\n",
    "#       {'histfile':'history_unfolded_snmf_a23657edf96a44331501d773db837a1c',\n",
    "#        'label': 'DR-SNMF, K=2, N=1000'},\n",
    "      {'histfile':'history_unfolded_snmf_ea1e7d485421e527486476ef696da2da',\n",
    "       'label': 'DR-SNMF, K=5, N=100'},\n",
    "      {'histfile':'history_unfolded_snmf_364ccd17a3e187bcccd30cfaa6bd9422',\n",
    "       'label': 'DR-SNMF, K=5, N=1000'}\n",
    "     ]\n",
    "\n",
    "colors=get_colors(4)\n",
    "\n",
    "only_big_models=True\n",
    "only_big_models=False\n",
    "if only_big_models:\n",
    "    exps=[exps[1],exps[3]]\n",
    "    colors=get_colors(2)\n",
    "\n",
    "plot_train_loss = False\n",
    "plot_train_loss = True\n",
    "xlim=None\n",
    "xlim=[0,400]\n",
    "# xlim=[100,200]\n",
    "# xlim=[300,350]\n",
    "ylim=None\n",
    "ylim=[0.0,0.125]\n",
    "# ylim=[0.03,0.04]\n",
    "# ylim=[0.,0.05]\n",
    "\n",
    "fig=plot_curves(datadir, exps, plot_train_loss=plot_train_loss, figsize=(3.5,3.5), colors=colors)\n",
    "\n",
    "if xlim is not None:\n",
    "    plt.xlim(xlim)\n",
    "if ylim is not None:\n",
    "    plt.ylim(ylim)\n",
    "plt.title('Learning curves, K=5, 10% of train')\n",
    "plt.legend()#loc='center left')#, bbox_to_anchor=(1, 0.5))\n",
    "plt.grid()\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Train loss or dev. loss')\n",
    "\n",
    "fig.savefig(fig_name, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 'LSTM, K=2, N=54' has 105071 trainable weights, best val_loss is 0.040835\n",
      "Model 'LSTM, K=2, N=244' has 1030181 trainable weights, best val_loss is 0.033863\n",
      "Model 'uSNMF, K=2, N=100' has 103002 trainable weights, best val_loss is 0.032010\n",
      "Model 'uSNMF, K=2, N=1000' has 1030002 trainable weights, best val_loss is 0.029525\n"
     ]
    },
    {
     "data": {
      "image/png": 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7gbqYAKnLT4mxVuatFc0bNVkae5LSMvduuubeXq9Xu3Tponv27CmZpNy8efMJ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\nNBpNUrRAaDSapGiB0Gg0SfkfDEUEIqNm3l4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb94410dfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# training data downsampled by 1, K=2\n",
    "datadir='data_setup_cc061d1dc474f44165340bb36f11b16d'\n",
    "\n",
    "fig_name='figures/learning_curves_K2_ds1_3-5x3-5.pdf'\n",
    "\n",
    "exps=[{'histfile':'history_lstm_46666e232751074bd609167dc440df8c',\n",
    "       'label': 'LSTM, K=2, N=54'},\n",
    "      {'histfile':'history_lstm_b6da76df68cf530d091aa499d61143de',\n",
    "       'label': 'LSTM, K=2, N=244'},\n",
    "      {'histfile':'history_unfolded_snmf_a45e86a1cc146e1e9d7a7f8100d9d2d7',\n",
    "       'label': 'uSNMF, K=2, N=100'},\n",
    "      {'histfile':'history_unfolded_snmf_a23657edf96a44331501d773db837a1c',\n",
    "       'label': 'uSNMF, K=2, N=1000'}\n",
    "     ]\n",
    "\n",
    "colors=get_colors(4)\n",
    "\n",
    "only_big_models=True\n",
    "only_big_models=False\n",
    "if only_big_models:\n",
    "    exps=[exps[1],exps[3]]\n",
    "    colors=get_colors(2)\n",
    "\n",
    "plot_train_loss = False\n",
    "plot_train_loss = True\n",
    "xlim=None\n",
    "xlim=[0,200]\n",
    "# xlim=[100,200]\n",
    "# xlim=[300,350]\n",
    "ylim=None\n",
    "ylim=[0.0,0.125]\n",
    "# ylim=[0.03,0.04]\n",
    "# ylim=[0.,0.05]\n",
    "\n",
    "fig=plot_curves(datadir, exps, plot_train_loss=plot_train_loss, figsize=(3.5,3.5), colors=colors)\n",
    "\n",
    "if xlim is not None:\n",
    "    plt.xlim(xlim)\n",
    "if ylim is not None:\n",
    "    plt.ylim(ylim)\n",
    "plt.title('Learning curves, K=2, 100% of train')\n",
    "plt.legend()#loc='center left')#, bbox_to_anchor=(1, 0.5))\n",
    "plt.grid()\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Train loss or dev. loss')\n",
    "\n",
    "fig.savefig(fig_name, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model 'LSTM, K=5, N=70' has 268007 trainable weights, best val_loss is 0.042590\n",
      "Model 'LSTM, K=5, N=250' has 2576507 trainable weights, best val_loss is 0.034364\n",
      "Model 'DR-SNMF, K=5, N=100' has 257205 trainable weights, best val_loss is 0.028558\n",
      "Model 'DR-SNMF, K=5, N=1000' has 2572005 trainable weights, best val_loss is 0.026553\n"
     ]
    },
    {
     "data": {
      "image/png": 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MUP7cAXyLsMy9T11z75EjR3L//fd7FJNff/01jz/+eI2uXVMqFBCqWvVCrQHh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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fb91f24c850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# training data downsampled by 1\n",
    "datadir='data_setup_cc061d1dc474f44165340bb36f11b16d'\n",
    "\n",
    "fig_name='figures/learning_curves_K5_ds1_3-5x3-5.pdf'\n",
    "\n",
    "exps=[ \\\n",
    "#       {'histfile':'history_lstm_46666e232751074bd609167dc440df8c',\n",
    "#        'label': 'LSTM, K=2, N=54'},\n",
    "#       {'histfile':'history_lstm_b6da76df68cf530d091aa499d61143de',\n",
    "#        'label': 'LSTM, K=2, N=244'},\n",
    "      {'histfile':'history_lstm_6a4fc9017283c9f89380f765a60087ce',\n",
    "       'label': 'LSTM, K=5, N=70'},\n",
    "      {'histfile':'history_lstm_4561bd13e267026c3f3d1c936b15f709',\n",
    "       'label': 'LSTM, K=5, N=250'},\n",
    "#       {'histfile':'history_unfolded_snmf_a45e86a1cc146e1e9d7a7f8100d9d2d7',\n",
    "#        'label': 'DR-SNMF, K=2, N=100'},\n",
    "#       {'histfile':'history_unfolded_snmf_a23657edf96a44331501d773db837a1c',\n",
    "#        'label': 'DR-SNMF, K=2, N=1000'},\n",
    "      {'histfile':'history_unfolded_snmf_ea1e7d485421e527486476ef696da2da',\n",
    "       'label': 'DR-SNMF, K=5, N=100'},\n",
    "      {'histfile':'history_unfolded_snmf_364ccd17a3e187bcccd30cfaa6bd9422',\n",
    "       'label': 'DR-SNMF, K=5, N=1000'}\n",
    "     ]\n",
    "\n",
    "colors=get_colors(4)\n",
    "\n",
    "only_big_models=False\n",
    "if only_big_models:\n",
    "    exps=[exps[1],exps[3]]\n",
    "    colors=get_colors(2)\n",
    "\n",
    "plot_train_loss = False\n",
    "plot_train_loss = True\n",
    "xlim=None\n",
    "xlim=[0,200]\n",
    "# xlim=[100,200]\n",
    "# xlim=[300,350]\n",
    "ylim=None\n",
    "ylim=[0.0,0.125]\n",
    "# ylim=[0.03,0.04]\n",
    "# ylim=[0.,0.05]\n",
    "\n",
    "fig=plot_curves(datadir, exps, plot_train_loss=plot_train_loss, figsize=(3.5,3.5), colors=colors)\n",
    "\n",
    "if xlim is not None:\n",
    "    plt.xlim(xlim)\n",
    "if ylim is not None:\n",
    "    plt.ylim(ylim)\n",
    "plt.title('Learning curves, K=5, 100% of train')\n",
    "plt.legend()#loc='center left')#, bbox_to_anchor=(1, 0.5))\n",
    "plt.grid()\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Train loss or dev. loss')\n",
    "\n",
    "fig.savefig(fig_name, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
